MATLAB_matrix_to_achieve_the_relevant_source_code

Simulnk动态仿真：详细介绍了在Simulnk环境下的动态仿真方法，简单易行，适合初学者(Simulnk Dynamic Simulation: Simulnk described in detail in the environment of dynamic simulation methods, is simple, suitable for beginners)

说明：  简单混合衰落信道 This mfile inputs an unmodulated sinewave through a simple Rayleigh two path fading channel and shows the output with phase, gain, and attenuation characteristics via animation. (simple mixed fading channel This mfile inputs an unmodulated sinewave through a simple two path Rayleigh fading channel and shows the output with phase, gain, and attenuation characteristics via animation.)

指数平滑法，一种数学建模的实用方法，对你的比赛，和应用很有帮助(Exponential smoothing，A good mathematical modeling tool, It is very helpful to your study MATLAB)

包含位置式PID算法、积分分离式PID，GPS和INS组合导航程序，可以实现模式识别领域的数据的分类及回归，预报误差法参数辨识-松弛的思想，车牌识别定位程序的部分功能，正确率可以达到98％。(It contains positional PID algorithm, integral separate PID, GPS and INS navigation program, You can achieve data classification and regression pattern recognition, Prediction Error Method for Parameter Identification- the idea of relaxation, Part of the license plate recognition locator feature, Accuracy can reach 98 .)

个慢跑者在平面上沿着他喜欢的路径跑步，突然一只狗攻击他，这只狗以不变的 速度跑向跑步者，狗的方向始终指向跑步者，计算并画出狗的运动轨迹（如需其他 条件，请自己假定）(A jogger in the plane running along the path of his favorite, a dog suddenly attacked him, the dog ran a constant speed runners, always point to the direction of the dog runners, calculate and draw the trajectory of the dog (For other conditions, at your own assumptions))

前后向平滑， music 主要包含经典music算法实现原理及其代码，然后在此基础上介绍了前后向平滑算法的实现及其代码和仿真结果(forward/backward spatial smoothing，MUSIC Consists mainly of classical music algorithm theory and its code, and then on this basis introduced before and after smoothing algorithm and code and simulation results)

传感器阵列波束优化设计与应用书籍第7章的matlab程序(Chapter 7 matlab program of book Sensor array beam optimization design and application)

Algorithms For Programmers Ideas And Source Code英文的，希望对大家有帮助。()

本代码包含M序列和GOLDEN序列的生成，以及基于MMSE的自适应多用户(This code contains the M sequence and GOLDEN sequence generation, as well as the MMSE-based adaptive multi-user)

For using this code need to use signal toolbox and general toolbox in your matlab Inpainting using sparse regularization. Consider the pepper image with many missing pixels. Assume that the image is noisy-free. (a) Inpaint the image by using orthogonal wavelet sparsity. (b) Inpaint the image by using a different sparsity prior. Based on the outputs, it is clear that the inpainting result obtain by Sobolev is much better than other methods (i.e. Orthogonal wavelet sparsity and Invariant wavelet sparsity).